A Study On Thresholding Methods Of Damaged Area Detection By Wildfires For Newly Launching Microsatellites

The increasing deployment of microsatellites in recent years has emerged as a cost-effective and efficient alternative to traditional high-weight satellites. These smaller satellites offer the advantage of frequent revisits, allowing for the monitoring and analysis of dynamic events and changes over time. This study specifically focuses on harnessing the potential of microsatellite imagery in disaster response scenarios, with a particular emphasis on the detection and assessment of wildfires. The research explores the development of methods for the rapid and effective detection of wildfires using microsatellite data, employing the Morphological Active Contours without Edge algorithm for analysis. Additionally, the study introduces the NEONSAT satellite constellation system, a significant initiative aimed at providing efficient analysis results for disaster response efforts. Simulated datasets were utilized to test the thresholding methods, and the evaluation of results was conducted using the Normalized Burned Ratio metric. Future research will focus on validating with actual microsatellite data and comprehensively assessing the performance of different algorithms for disaster response applications.